Comparing strings

Cleaning Data in Python

Adel Nehme

Content Developer @ DataCamp

In this chapter

Chapter 4 - Record linkage

Minimum edit distance

Least possible amount of steps needed to transition from one string to another

Minimum edit distance

Least possible amount of steps needed to transition from one string to another

Minimum edit distance

Minimum edit distance so far: 2

Minimum edit distance

Minimum edit distance: 5

Minimum edit distance

Minimum edit distance algorithms

Algorithm	Operations
Damerau-Levenshtein	insertion, substitution, deletion, transposition
Levenshtein	insertion, substitution, deletion
Hamming	substitution only
Jaro distance	transposition only
...	...

Possible packages: nltk, thefuzz, textdistance ..

Minimum edit distance algorithms

Algorithm	Operations
Damerau-Levenshtein	insertion, substitution, deletion, transposition
*Levenshtein*	**insertion, substitution, deletion**
Hamming	substitution only
Jaro distance	transposition only
...	...

Possible packages: thefuzz

Simple string comparison

# Lets us compare between two strings
from thefuzz import fuzz


# Compare reeding vs reading
fuzz.WRatio('Reeding', 'Reading')

Partial strings and different orderings

# Partial string comparison
fuzz.WRatio('Houston Rockets', 'Rockets')

# Partial string comparison with different order
fuzz.WRatio('Houston Rockets vs Los Angeles Lakers', 'Lakers vs Rockets')

Comparison with arrays

# Import process
from thefuzz import process

# Define string and array of possible matches
string = "Houston Rockets vs Los Angeles Lakers"
choices = pd.Series(['Rockets vs Lakers', 'Lakers vs Rockets', 
                     'Houson vs Los Angeles', 'Heat vs Bulls'])

process.extract(string, choices, limit = 2)

[('Rockets vs Lakers', 86, 0), ('Lakers vs Rockets', 86, 1)]

Collapsing categories with string similarity

Chapter 2

Use .replace() to collapse "eur" into "Europe"

What if there are too many variations?

"EU", "eur", "Europ", "Europa", "Erope", "Evropa"...

String similarity!

Collapsing categories with string matching

print(survey['state'].unique())

id          state
0      California
1            Cali
2      Calefornia
3      Calefornie
4      Californie
5       Calfornia
6      Calefernia
7        New York
8   New York City
...

categories

  state
0 California
1 New York

Collapsing all of the state

# For each correct category
for state in categories['state']:

  # Find potential matches in states with typoes
    matches = process.extract(state, survey['state'], limit = survey.shape[0])

    # For each potential match match
    for potential_match in matches:
       # If high similarity score
        if potential_match[1] >= 80:

          # Replace typo with correct category
          survey.loc[survey['state'] == potential_match[0], 'state'] = state

Record linkage

record linkage

Let's practice!

Cleaning Data in Python